Uplink-Downlink Duality for Beamforming in Integrated Sensing and Communications

2025-09-16

AI Summary

This paper addresses integrated sensing and communication (ISAC) systems, tackling the problem of jointly optimizing downlink beamforming and power allocation to minimize the Bayesian Cramér–Rao bound (BCRB) for parameter estimation—subject to users’ SINR constraints. The method establishes a key equivalence: maximizing beam power in the sensing direction is equivalent to minimizing the BCRB; extends uplink–downlink duality to ISAC by constructing a novel dual model incorporating negative noise power and beam structure constraints; and leverages BCRB analysis, MIMO beamforming, and iterative optimization to formulate an efficient, tractable dual framework. Experimental results demonstrate that the proposed approach significantly enhances sensing accuracy while strictly satisfying communication requirements, thereby providing both theoretical foundations and practical algorithms for resource co-optimization in ISAC systems.

Technology Category

Search and Optimization: Mixed Discrete/Continuous SearchConstraint Satisfaction and Optimization: Mixed Discrete/Continuous OptimizationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructuresUser Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationSecurity and Privacy: Authentication, authorization, and access control

Abstract

This paper considers the beamforming and power optimization problem for a class of integrated sensing and communications (ISAC) problems that utilize the communication signals simultaneously for sensing. We formulate the problem of minimizing the Bayesian Cramér-Rao bound (BCRB) on the mean-squared error of estimating a vector of parameters, while satisfying downlink signal-to-interference-and-noise-ratio constraints for a set of communication users at the same time. The proposed optimization framework comprises two key new ingredients. First, we show that the BCRB minimization problem corresponds to maximizing beamforming power along certain sensing directions of interest. Second, the classical uplink-downlink duality for multiple-input multiple-output communications can be extended to the ISAC setting, but unlike the classical communication problem, the dual uplink problem for ISAC may entail negative noise power and needs to include an extra condition on the uplink beamformers. This new duality theory opens doors for an efficient iterative algorithm for optimizing power and beamformers for ISAC.

Problem

• Minimizing BCRB for sensing parameter estimation
• Satisfying SINR constraints for communication users
• Extending uplink-downlink duality to ISAC beamforming

Innovation

• Uplink-downlink duality extension to ISAC
• Minimizing BCRB via beamforming power maximization
• Iterative algorithm with negative noise power